Publication of nuclear magnetic resonance experimental data with semantic web technology and the application thereof to biomedical research of proteins. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Yokochi, Masashi; Kobayashi, Naohiro; Ulrich, Eldon; Kinjo, Akira; Iwata, Takeshi; Ioannidis, Yannis; Livny, Miron; Markley, John; Nakamura, Haruki; Kojima, Chojiro; Fujiwara, Toshimichi
• Is Part Of: Journal of biomedical semantics. Volume 7:Issue 1(2016)
• Page Start: 1
• Page End: 4
• Publication Date: 2016-12
• Subjects: NMR -- BMRB -- Database -- XML -- RDF; Semantics -- Periodicals; Medicine -- Research -- Periodicals; Biology -- Research -- Periodicals; Computer systems -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1186/s13326-016-0057-1
• Languages: English
• ISSNs: 2041-1480

Abstract

Abstract Background The nuclear magnetic resonance (NMR) spectroscopic data for biological macromolecules archived at the BioMagResBank (BMRB) provide a rich resource of biophysical information at atomic resolution. The NMR data archived in NMR-STAR ASCII format have been implemented in a relational database. However, it is still fairly difficult for users to retrieve data from the NMR-STAR files or the relational database in association with data from other biological databases. Findings To enhance the interoperability of the BMRB database, we present a full conversion of BMRB entries to two standard structured data formats, XML and RDF, as common open representations of the NMR-STAR data. Moreover, a SPARQL endpoint has been deployed. The described case study demonstrates that a simple query of the SPARQL endpoints of the BMRB, UniProt, and Online Mendelian Inheritance in Man (OMIM), can be used in NMR and structure-based analysis of proteins combined with information of single nucleotide polymorphisms (SNPs) and their phenotypes. Conclusions We have developed BMRB/XML and BMRB/RDF and demonstrate their use in performing a federated SPARQL query linking the BMRB to other databases through standard semantic web technologies. This will facilitate data exchange across diverse information resources.